A high-standard farmland road base state intelligent monitoring and early warning method and system

Abstract

The application discloses a high-standard farmland road base state intelligent monitoring and early warning method and system, and belongs to the technical field of road maintenance. The method obtains the surface layer and internal pH value, water content and mechanical response value of the lime modified base, constructs a chemical integrity degradation factor based on the double pH difference to represent the weed suppression function decay rate, and constructs a hydraulic integrity degradation factor based on the correlation fluctuation of the water content and the mechanical response to represent the water stability damage degree. The double degradation factors are compared with the multi-level threshold value, and the early warning level is determined in combination with the single parameter critical threshold value. The remaining time length of the weed suppression function failure is predicted according to the time sequence evolution of the chemical integrity degradation factor. If the remaining time length is less than the maintenance window, the base alkali supplement amount is obtained according to the difference between the second pH value and the target recovery value, and the early warning instruction is generated after the water content correction, so that the chemical and hydraulic integrity are cooperatively monitored, the performance degradation is predicted, and the accurate maintenance guidance is realized.

Description

Technical Field

[0001] This application relates to the field of road maintenance technology, specifically to an intelligent monitoring system and early warning method and system for the condition of the base layer of high-standard farmland roads. Background Technology

[0002] High-standard farmland roads use lime-modified soil base courses, which utilize their highly alkaline environment to suppress weed growth and ensure structural stability. However, lime-modified base courses face a dual degradation mechanism during service: on the one hand, the surface lime soil is leached by rainwater and carbonized by the atmosphere, leading to the loss of alkaline substances and a gradual decline in its weed-suppressing function; on the other hand, rainwater infiltration causes fluctuations in the moisture content of the base course, resulting in water stability damage and structural fatigue under repeated vehicle loads.

[0003] Existing road monitoring technologies suffer from the following main shortcomings: First, they rely on a single monitoring parameter, focusing primarily on pavement structural deformation or surface damage, lacking direct monitoring methods for the degradation process of lime-modified base courses in an alkaline environment, thus failing to assess the degree of weed suppression function loss. Second, the monitoring dimensions are fragmented, with pH monitoring, moisture content monitoring, and mechanical response monitoring operating independently, failing to establish a correlation mechanism between chemical degradation and hydraulic-mechanical damage. Third, the early warning mode is passive, often using fixed thresholds to trigger alarms, unable to predict performance degradation trends, leading to delayed maintenance decisions, often requiring repairs only after weeds have penetrated or the structure has been damaged, missing the optimal maintenance opportunity. Fourth, maintenance guidance is rudimentary, with the estimation of alkali replenishment lacking scientific basis, easily resulting in material waste or insufficient repair. Summary of the Invention

[0004] This application provides an intelligent monitoring system and early warning method and system for the condition of high-standard farmland road base, in order to alleviate the aforementioned technical problems.

[0005] Firstly, this application provides a method for intelligent monitoring and early warning of the condition of the base course of high-standard farmland roads, including: Data from sensor arrays deployed at different vertical depths within the cross-section of a lime-modified base layer are obtained, including at least the first pH value at the surface of the base layer, the second pH value at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load. Based on the relative attenuation difference between the first pH value and the second pH value during a continuous monitoring period, a chemical integrity degradation factor characterizing the rate of loss of the grass-suppressing function of the base layer is constructed; based on the correlation fluctuation characteristics of volumetric water content and mechanical response value within the same time window, a hydraulic integrity degradation factor characterizing the degree of water stability damage of the base layer is constructed. The chemical integrity degradation factor and the hydraulic integrity degradation factor are compared with the preset multi-level early warning threshold range, and the first pH value, the second pH value, the volumetric water content and the mechanical response value are compared with the corresponding single-parameter critical thresholds. The early warning level is determined according to the combination of the comparison results. Based on the temporal evolution trajectory of the chemical integrity deterioration factor within the historical cycle, a temporal extrapolation algorithm is used to predict the remaining time before the weed suppression function reaches critical failure. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the base layer, generating an early warning instruction carrying the recommended alkali replenishment amount information.

[0006] Secondly, this application also provides an intelligent monitoring and early warning system for the condition of high-standard farmland road base layers, the system comprising: The data acquisition module is used to acquire sensor array data at different vertical depths within the cross section of the lime-modified base layer, including at least the first pH value located on the surface of the base layer, the second pH value located at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load. The data construction module is used to construct a chemical integrity degradation factor characterizing the rate of loss of the grass-suppressing function of the base layer based on the relative decay difference between the first pH value and the second pH value in a continuous monitoring period; and to construct a hydraulic integrity degradation factor characterizing the degree of water stability damage of the base layer based on the correlation fluctuation characteristics of volumetric water content and mechanical response value within the same time window. The data processing module is used to compare the chemical integrity degradation factor and the hydraulic integrity degradation factor with the preset multi-level early warning threshold range, and at the same time compare the first pH value, the second pH value, the volumetric water content and the mechanical response value with the corresponding single-parameter critical threshold, and determine the early warning level according to the combination of comparison results. The data processing module is used to predict the remaining time before the weed suppression function reaches its critical failure based on the temporal evolution trajectory of the chemical integrity degradation factor within the historical cycle using a temporal extrapolation algorithm. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the base layer, generating an early warning instruction carrying the recommended alkali replenishment amount information.

[0007] One of the above technical solutions has the following advantages or beneficial effects: This application provides an intelligent monitoring and early warning method for the condition of high-standard farmland road base layers. By constructing a dual-dimensional deterioration assessment system based on chemical and hydraulic factors, it achieves a shift in maintenance mode from passive repair to proactive prevention. First, this application achieves precise quantitative assessment of the decay of the weed-suppressing function of lime-modified base layers. Traditional technologies only monitor surface pH changes, failing to distinguish between surface loss and internal reserves. This application, through the vertical deployment of dual pH sensors at the surface and interior, constructs a relative attenuation difference index, accurately capturing the dynamic process of alkaline substances migrating and diffusing from the surface to the interior, thereby quantifying the rate of weed-suppressing function loss and overcoming the technical deficiency of a single monitoring point being unable to assess functional reserves. Second, this application establishes a correlation identification mechanism for water stability damage. Existing technologies treat moisture content and mechanical response as independent parameters, neglecting the damage amplification effect of the coupling effect of moisture and load. This application, by calculating the correlation fluctuation characteristics of volumetric moisture content and mechanical response values ​​within the same window, identifies water-sensitive abrupt changes caused by water intrusion, enabling early warning of water damage risks and overcoming the early warning lag problem caused by parameter fragmentation. Simultaneously, this application achieves multi-level, multi-mode collaborative early warning. By comparing dual degradation factors with multi-level threshold ranges and combining this with single-parameter critical threshold determination, a combined early warning logic of comprehensive indicators and single-parameter verification is formed. This avoids misjudgment based on a single indicator and ensures timely response when key parameters exceed limits, thus improving the accuracy and reliability of early warnings. Furthermore, this application provides a predictive maintenance time window and quantitative guidance. Based on the temporal evolution trajectory of the chemical integrity degradation factor, time extrapolation can predict the remaining duration of critical failure of the weed suppression function, reserving decision-making time for maintenance plans. When the remaining duration enters the maintenance window, the system calculates the alkali replenishment amount based on the internal pH value and introduces a moisture content correction coefficient to compensate for diffusion loss, achieving precise decision-making and avoiding resource waste or insufficient repair caused by experience-based maintenance. Attached Figure Description

[0008] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0009] Figure 1 A schematic diagram illustrating the steps of the intelligent monitoring and early warning method for the base condition of high-standard farmland roads provided in this application embodiment.

[0010] Figure 2 A schematic diagram of the module connection of the intelligent monitoring and early warning system for the base condition of high-standard farmland roads provided in this application embodiment. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Features thus defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more unless otherwise explicitly specified.

[0013] like Figure 1 As shown in the embodiment of this application, a method for intelligent monitoring and early warning of the condition of the base course of high-standard farmland roads is provided, characterized by comprising: S1: Acquire sensor array data at different vertical depths within the cross-section of the lime-modified base layer, including at least the first pH value at the surface of the base layer, the second pH value at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load.

[0014] In this embodiment, lime-modified base course refers to a road structure base course treated with lime stabilization, possessing alkali storage and mechanical bearing functions; vertical depth refers to the vertical distance from the top of the base course downwards, in millimeters (mm); sensor array data refers to a multi-dimensional monitoring data set reflecting the internal environment and mechanical state of the base course, collected synchronously by multiple types of sensors; the first pH value refers to the pH value of the functionally deteriorated area on the surface of the base course, dimensionless; the second pH value refers to the pH value of the alkali storage area in the middle of the base course, dimensionless; volumetric moisture content refers to the proportion of water per unit volume within the base course, expressed as a percentage (%); mechanical response value refers to the strain value, stress value, or settlement value generated by the base course under vehicle load, wherein the strain value is used to characterize the internal stress state of the base course, in microstrain (με), and the stress value or settlement value is used to characterize the deflection or settlement deformation of the top surface of the base course, in millimeters (mm).

[0015] The method for acquiring sensor array data at different vertical depths within the cross-section of a lime-modified base course includes: dividing the vertical cross-section of the lime-modified base course into a surface functional degradation zone, a central alkali reserve zone, and a bottom hydraulic disturbance zone according to a preset vertical proportion of the total thickness of the base course; deploying a first pH sensor in the surface functional degradation zone to collect a first pH value; deploying a second pH sensor in the central alkali reserve zone to collect a second pH value; the vertical projections of the first and second pH sensors coincide, and their vertical distance is not less than a preset minimum distance threshold, which is set based on the total thickness of the lime-modified base course; deploying volumetric moisture content sensors at the same vertical depth as the first and second pH sensors to collect the volumetric moisture content of the base course; deploying a mechanical response sensor on the top surface of the base course, at a position coinciding with the vertical projection of the first pH sensor, to collect the mechanical response value under vehicle load; controlling each sensor to synchronously collect data according to preset acquisition rules, wherein the mechanical response sensor is awakened and performs acquisition using an event triggering method with preset trigger conditions.

[0016] In this embodiment, lime-modified base course refers to a road load-bearing base course that uses lime as a stabilizer and is compacted, and is the monitoring object; vertical depth refers to the distance from the top surface of the lime-modified base course downwards in the vertical direction, in millimeters (mm); sensor array data refers to a multi-dimensional monitoring data set collected synchronously by multiple sensors at different depths; surface functional degradation zone refers to the surface area of ​​the lime-modified base course that is most significantly affected by atmospheric carbonization, water infiltration, and traffic loads from top to bottom in the vertical direction; central alkali reserve zone refers to the core area located below the surface functional degradation zone, which is responsible for alkali reserve supply and structural stability; bottom hydraulic disturbance zone refers to the area near the bottom of the base course that is affected by groundwater and seepage; the first pH value refers to the surface functional degradation zone. The pH monitoring value of the deterioration zone is dimensionless; the second pH value refers to the pH monitoring value of the central alkali reserve zone, which is also dimensionless; the volumetric moisture content refers to the volume ratio of water in a unit volume of base material, expressed as a percentage (%); the mechanical response value refers to the strain, stress, or deflection response value of the base under vehicle load, expressed in microstrain (με) or millimeters (mm); the preset minimum spacing threshold refers to the minimum vertical distance set to ensure that the first pH sensor and the second pH sensor can effectively distinguish the pH differences in different areas, determined based on the total thickness of the base; the event triggering mode refers to the automatic wake-up and acquisition mode of the mechanical response sensor when the preset vehicle load triggering conditions are met, and the sleep acquisition mode when there is no trigger.

[0017] Specifically, along the vertical cross-section of the lime-modified base course, the base course is divided into a surface functional degradation zone, a middle alkali reserve zone, and a bottom hydraulic disturbance zone according to a preset vertical proportion of the total base course thickness. The degree of influence from the external environment (atmosphere, rainfall, groundwater) and traffic loads varies significantly at different depths along the base course. This layered design can accurately locate the sources of degradation in each functional area. The surface layer focuses on functional degradation caused by atmospheric carbonization and moisture infiltration, the middle layer focuses on the alkali reserve depletion state, and the bottom layer focuses on the impact of groundwater disturbance. This effectively avoids misjudgments of degradation causes caused by mixed-layer monitoring and provides a zoning basis for subsequent targeted sensor deployment and precise collection of characteristic data, improving the targeting and accuracy of monitoring.

[0018] A first pH sensor is deployed in the surface functional degradation zone to collect the first pH value; a second pH sensor is deployed in the central alkali reserve zone to collect the second pH value; at the same vertical depth as the first and second pH sensors, corresponding volumetric moisture sensors are deployed to collect the volumetric moisture content of the base layer at the corresponding locations. Layered deployment of pH sensors and other sensors enables precise matching monitoring of multiple depths and parameters in the vertical direction of the base layer. The layered deployment of pH sensors can separately capture the difference in alkalinity decay between the surface degradation zone and the central alkali reserve zone, directly reflecting the consumption rate of the alkali reserve and the interlayer gradient. The volumetric moisture sensor, deployed at the same depth as the pH sensor, can simultaneously acquire moisture conditions at the corresponding depth, clarifying the influence of moisture migration on alkalinity decay, avoiding cross-interference of parameters at different depths, and providing accurate layered matching data for subsequent degradation factor construction and damage mechanism analysis.

[0019] The vertical projections of the first and second pH sensors are aligned to ensure that their vertical distance is not less than a preset minimum distance threshold, which is set based on the total thickness of the lime-modified base layer. This alignment ensures that the two sensors are vertically aligned, eliminating monitoring errors caused by differences in horizontal position. It also makes pH monitoring data from the surface and middle layers comparable, thereby accurately capturing alkaline gradient changes at different vertical depths within the same spatial location and avoiding misjudgments of alkali decay patterns due to horizontal offset.

[0020] The vertical distance between the two sensors is not less than a preset minimum distance threshold. This preset minimum distance threshold is determined based on the sensor measurement accuracy and pH decay rate. To achieve the minimum distance for effective monitoring, it ensures that the pH values ​​collected by the two sensors have a difference that can be effectively distinguished. This avoids the situation where the pH monitoring data of the surface and the middle layer are too similar due to the small distance, which would fail to reflect the change in alkaline gradient. At the same time, it prevents the sensors from interfering with each other due to insufficient distance, ensuring that the monitoring data can truly reflect the difference between surface degradation and the alkaline reserve in the middle layer, and providing reliable gradient data for the subsequent construction of chemical integrity degradation factors.

[0021] A mechanical response sensor is deployed on the top surface of the substrate, at a location coinciding with the vertical projection of the first pH sensor, to collect mechanical response values ​​under vehicle loads. This deployment method allows the mechanical response sensor and the first pH sensor to be in the same vertical projection space, achieving synchronous matching monitoring of the surface pH state and the top surface mechanical response at the same spatial location. This enables precise correlation between vehicle load disturbance and surface alkalinity degradation, clarifying the impact of load on surface pH decay and functional degradation, avoiding misjudgments of coupling relationships caused by spatial offset, and providing accurate spatial matching data for subsequent construction of hydraulic integrity degradation factors and multi-parameter correlation analysis.

[0022] The system controls all sensors to synchronously collect data according to preset acquisition rules. The mechanical response sensor uses an event-triggered method with preset trigger conditions for wake-up and data acquisition. Preset acquisition rules refer to pre-setting uniform standards for the acquisition frequency, acquisition duration, and data transmission interval of each sensor, ensuring that all sensors acquire and transmit data synchronously in the same time dimension. The event-triggered method of the mechanical response sensor means that the sensor only wakes up from sleep mode and starts acquiring data when preset trigger conditions are met (such as vehicle load reaching a preset threshold or a vehicle passing the monitoring point); otherwise, it remains in low-power sleep mode. This setting ensures the temporal consistency of data such as pH value, volumetric water content, and mechanical response value through synchronous acquisition by all sensors, facilitating subsequent analysis of the coupling correlation of various parameters and avoiding data analysis errors caused by temporal deviations. Simultaneously, the event-triggered method of the mechanical response sensor can accurately capture the mechanical response characteristics under each vehicle load, avoiding invalid data redundancy caused by continuous acquisition, reducing sensor energy consumption, extending the service life of the monitoring system, and balancing monitoring accuracy and energy efficiency.

[0023] It is worth noting that the preset minimum spacing threshold can be obtained through the following steps: obtaining the measurement accuracy values ​​of the first pH sensor and the second pH sensor, as well as the annual average pH decay rate of the lime-modified base layer under preset conditions; the annual average pH decay rate is obtained based on the local annual average rainfall pH value, atmospheric carbon dioxide concentration, and base layer lime content through a preset estimation model; based on the measurement accuracy value and the preset monitoring period, determining the minimum pH difference that can be effectively distinguished by the sensors within the preset monitoring period, wherein the minimum pH difference is not less than a preset multiple of the measurement accuracy value; based on the annual average pH decay rate and the minimum pH difference, calculating the vertical distance required to generate the minimum pH difference between the surface functional deterioration zone and the central alkali reserve zone; multiplying the vertical distance by a preset safety factor to obtain the preset minimum spacing threshold, wherein the preset safety factor is greater than 1, and preferably ranges from 1.2 to 2.5.

[0024] In this embodiment, the preset minimum spacing threshold refers to the minimum vertical distance set to ensure that the first pH sensor and the second pH sensor can effectively distinguish the pH difference between the surface functional degradation zone and the central alkali reserve zone, with the unit being millimeters (mm); the measurement accuracy value refers to the minimum pH change that the pH sensor can accurately identify, which is dimensionless; the annual average pH decay rate refers to the average annual decrease in pH value of the lime-modified base layer under local natural environment and material parameters, which is dimensionless and is measured annually (a -1 The preset prediction model refers to the model constructed based on the local annual average rainfall pH value, atmospheric carbon dioxide concentration, and base lime content to calculate the annual average pH decay rate; the preset monitoring period refers to the continuous monitoring time set by the system, in years (a); the minimum pH difference refers to the minimum pH change difference that the sensor can effectively identify within the preset monitoring period, dimensionless; the preset multiple refers to the multiple of the minimum pH difference relative to the measurement accuracy value set to ensure resolution reliability, with a value greater than 1; the vertical distance refers to the vertical distance between the surface functional deterioration zone and the central alkali reserve zone required to form a detectable minimum pH difference, in millimeters (mm); the preset safety factor refers to the amplification factor set to improve engineering reliability, with a value greater than 1, dimensionless.

[0025] The method for constructing the prediction model includes the following steps: Step 101: Data Acquisition and Calibration.

[0026] A total of 27 samples of lime-modified base course were selected, including samples with three different lime content (3%, 5%, 8%), three different compaction degrees (93%, 95%, 98%), and three different porosities (8%, 12%, 15%). Service environments with different rainfall pH values ​​(4.5, 5.6, 7.0) and different atmospheric CO2 concentrations (400ppm, 500ppm, 600ppm) were simulated. Each sample was monitored for curing for one year, and the annual average pH decay rate of each sample was recorded, resulting in 108 sets of experimental data.

[0027] A multivariate nonlinear regression model was constructed, combined with calibration coefficients from indoor experiments. The specific formula is as follows: ; In the formula, v pH The average annual pH decay rate of lime-modified substrate, in pH units per year (pH / year); pH rain The pH value is the local average annual rainfall, dimensionless (4.5 ≤ pH ≤ 7.0). ω is the annual average atmospheric carbon dioxide concentration, in ppm (400≤C≤600); ω is the designed lime content, dimensionless (3≤ω≤8); K is the base layer compaction degree, dimensionless (93≤K≤98); n is the base layer porosity, dimensionless (8≤n≤15); k1 is the influence coefficient of rainfall pH value, dimensionless, with a value of 0.25 (calibrated through 100 sets of indoor tests with different rainfall pH values); k2 is the influence coefficient of atmospheric CO2 concentration, dimensionless, with a value of 0.18 (calibrated through indoor simulations of different CO2 concentrations). (calibrated by tests); k3 is the influence coefficient of lime content, dimensionless, with a value of 0.12 (calibrated by comparative tests of different lime contents); k4 is the influence coefficient of compaction degree, dimensionless, with a value of 0.08 (calibrated by tests of different compaction degrees); k5 is the influence coefficient of porosity, dimensionless, with a value of 0.05 (calibrated by tests of different porosities); v0 is the baseline decay rate, unit: pH units / year (pH / year), with a value of 0.1 (the natural pH decay rate of the base layer without external interference, calibrated by blank tests).

[0028] Step 102: Coefficient calibration.

[0029] Substituting the 108 sets of experimental data into the above multivariate nonlinear regression formula, the least squares method was used for fitting calculation to determine the specific values ​​of k1, k2, k3, k4, k5 and v0 (i.e., the above calibration values), ensuring the model fit R. 2 ≥0.92 (the higher the goodness of fit, the higher the accuracy of the model prediction).

[0030] Step 103: Model Validation.

[0031] Six groups of samples that were not included in the fitting (covering different parameter combinations) were selected, and their input parameters were substituted into the calibrated model. The annual average pH decay rate predicted by the model was calculated and compared with the actual monitored annual average pH decay rate to ensure that the relative error was ≤8% and to verify the accuracy and reliability of the model.

[0032] Step 104: Model solidification.

[0033] The calibrated formulas, parameter ranges, and coefficients are solidified into the system. When used in the field, only the local annual average rainfall pH value, atmospheric CO2 concentration, design lime content, compaction degree, and porosity need to be input to automatically calculate the annual average pH decay rate v, providing basic parameters for subsequent steps.

[0034] The specific implementation process is as follows: First, the rated measurement accuracy values ​​of the first pH sensor and the second pH sensor are read and recorded to ensure that the measurement accuracy of the two sensors is consistent or at the same accuracy level. At the same time, through on-site environmental monitoring and material parameter input, the annual average pH decay rate of the lime-modified base course under the preset natural service conditions is obtained. This annual average pH decay rate is calculated by a preset estimation model in combination with the local annual average rainfall pH value, the annual average atmospheric carbon dioxide concentration and the designed lime content, which can accurately reflect the actual rate of alkalinity decay of the base course.

[0035] Based on the established sensor measurement accuracy and the preset monitoring period set by the system, a distinguishability calculation is performed to determine the minimum pH difference that the sensor can stably and effectively distinguish within the entire preset monitoring period. This minimum pH difference is not less than a preset multiple of the sensor measurement accuracy, ensuring that the monitoring data has statistical significance and engineering distinguishability, and avoiding the inability to effectively distinguish monitoring results due to the difference being too small.

[0036] After obtaining the minimum pH difference value, the annual average pH decay rate is used as the basis for calculation. The minimum pH difference value is substituted into the vertical distance calculation formula to obtain the theoretical vertical distance required for the pH difference between the surface functional deterioration zone and the central alkali reserve zone to be effectively identified by the sensor. This distance provides the basis for determining the minimum spacing in the future.

[0037] To mitigate the impact of construction errors, material inhomogeneity, and environmental fluctuations on monitoring results, the theoretical vertical distance is amplified and compensated. By setting a preset safety factor greater than 1, the theoretical vertical distance is multiplied by the safety factor to obtain the preset minimum spacing threshold that meets the actual needs of the project. This ensures that the sensor spacing is reasonable and that the monitoring data can truly and accurately reflect the alkalinity change pattern of the base layer, thus providing a guarantee for the accurate acquisition of subsequent monitoring parameters.

[0038] Understandably, this step achieves simultaneous monitoring of environmental and mechanical parameters at multiple depths—surface, middle, and bottom—by deploying sensor arrays in sections along the vertical cross-section of the lime-modified base course. This solves the problems of traditional road base course monitoring, which only collects surface data, cannot reflect the internal alkali reserve decay and moisture migration patterns, and is difficult to distinguish between environmental degradation and load damage. By using the same projection and equidistant site deployment, the monitoring space is kept consistent. An event-triggered mechanism is used to achieve accurate wake-up data collection under vehicle loads, providing real, synchronous, and multi-dimensional raw data for subsequent base course health status assessment, alkali reserve prediction, and lifespan analysis.

[0039] S2: Based on the relative attenuation difference between the first pH value and the second pH value in a continuous monitoring period, a chemical integrity degradation factor characterizing the rate of loss of the grass-suppressing function of the base layer is constructed; based on the correlation fluctuation characteristics of volumetric water content and mechanical response value within the same time window, a hydraulic integrity degradation factor characterizing the degree of water stability damage of the base layer is constructed.

[0040] The chemical integrity degradation factor, which characterizes the rate of loss of weed suppression function in the basement, is constructed based on the relative attenuation difference between the first and second pH values ​​during a continuous monitoring period. This includes: calculating the attenuation rate difference between the first and second pH values ​​within a preset sliding time window, where the attenuation rate difference characterizes the asynchronous leaching of alkalinity between the surface and internal lime-soil layers of the basement; performing a time-dimensional cumulative calculation on the attenuation rate difference within the preset sliding time window to obtain the cumulative damage amount of the alkaline environment in the basement; generating a mutation penalty term when the current ratio of the first and second pH values ​​is lower than a preset critical gradient ratio; the preset critical gradient ratio is determined based on a preset ratio between the surface and internal pH values ​​of the lime-modified basement under critical failure of weed suppression function; and normalizing the cumulative damage amount and the mutation penalty term to obtain the chemical integrity degradation factor.

[0041] In this embodiment, the continuous monitoring period refers to the total duration of continuous pH data collection on the lime-modified base layer at a fixed time step, in days (d); the relative attenuation difference refers to the difference in attenuation rates between the first pH value and the second pH value within the same time window, used to characterize the asynchronous degree of alkaline attenuation between the surface and interior, and is dimensionless; the chemical integrity degradation factor refers to an index used to quantitatively evaluate the rate at which the lime-modified base layer loses its weed-suppressing function due to alkaline attenuation, with a value range of [0, 1], the larger the value, the more severe the degradation, and is dimensionless; the preset sliding time window refers to a continuous pH data segment that is rolled out at a fixed step, used to dynamically calculate the attenuation rate, the window length is set according to the monitoring frequency, and is in hours (h) or days (d); the attenuation rate difference refers to the difference between the pH attenuation rate of the surface functional degradation zone and the pH attenuation rate of the central alkali reserve zone, used to characterize the surface of the base layer. The asynchronous nature of alkaline loss between the layer and the internal lime-soil is dimensionless; the cumulative damage refers to the total value obtained by accumulating the difference in decay rate along the time dimension within a preset sliding time window, used to characterize the overall deterioration degree of the alkaline environment of the base layer over time, and is dimensionless; the current ratio refers to the real-time ratio of the first pH value to the second pH value at the same moment, and is dimensionless; the preset critical gradient ratio refers to the critical ratio value of the surface and internal pH when the lime-modified base layer is in a critical failure state of weed suppression function, calibrated by indoor test and engineering application standards, and is dimensionless; the mutation penalty term refers to the nonlinear penalty increment introduced when the pH ratio of the surface and internal layers is lower than the critical gradient, used to amplify the rapid deterioration characteristics caused by the abrupt change in alkaline gradient, and is dimensionless; the normalization treatment refers to the standardization treatment of mapping the superposition result of the cumulative damage and the mutation penalty term to the interval [0, 1], eliminating the difference in dimensions and numerical magnitudes, and ensuring the comparability of indicators.

[0042] The specific implementation process is as follows: Based on the relative attenuation difference between the first and second pH values ​​within a continuous monitoring period, a chemical integrity degradation factor characterizing the rate of loss of weed-suppressing function at the base layer is constructed. First, the time series of the first and second pH values ​​are segmented according to a preset sliding time window. The attenuation rate of the first and second pH values ​​within the window is calculated using a linear fitting method. The difference between the two attenuation rates is then obtained. The specific calculation formula is as follows: ; ; In the formula, Δv(t) represents the difference in decay rates at time t, in units of pH per time (pH / a); v surf (t) represents the instantaneous decay rate of surface pH, in units of pH per time (pH / a); v mid (t) represents the instantaneous rate of pH decay at the midpoint, in units of pH per time (pH / a); pH surf(t) represents the first pH value at time t, which is dimensionless; pH mid (t) is the second pH value at time t, dimensionless; t is time, in years (a).

[0043] By using a sliding time window to segment and calculate the difference in decay rate, the dynamic changes in alkaline decay of the surface and interior of the base layer at different time periods can be dynamically captured, and the asynchronous nature can be quantitatively characterized. This provides accurate time-series difference data for subsequent degradation analysis and avoids misjudgment of degradation patterns caused by monitoring a single time point.

[0044] Subsequently, the aforementioned attenuation rate differences are accumulated point by point along the time dimension within a preset sliding time window to obtain the cumulative damage amount that reflects the continuous deterioration of the alkaline environment at the base level. By accumulating point by point along the time dimension, the instantaneous attenuation difference can be transformed into a long-term cumulative quantitative indicator of deterioration, intuitively reflecting the gradual deterioration process of the alkaline environment at the base level over time. This provides data support for the accurate construction of subsequent deterioration factors, ensuring the comprehensiveness of the deterioration characterization.

[0045] Simultaneously, the current ratio of the first pH value to the second pH value at the same moment is calculated in real time. This current ratio is then compared in real time with a preset critical gradient ratio. The preset critical gradient ratio is determined based on the pH ratio between the surface functional degradation zone and the central alkali reserve zone of the lime-modified base course under the critical failure state of its weed-suppressing function. The preset critical gradient ratio closely matches the actual working condition of the base course's weed-suppressing function failure. By comparing the current ratio with the critical ratio in real time, it is possible to accurately identify whether the base course's weed-suppressing function has entered the critical failure stage, providing a scientific basis for the triggering of subsequent mutation penalty items and avoiding missing key monitoring nodes of deterioration mutations.

[0046] When the current ratio of the first pH value to the second pH value is lower than the preset critical gradient ratio, the mutation penalty term generation process is immediately initiated and the corresponding mutation penalty term is obtained. The setting of the mutation penalty term can effectively amplify the degradation effect caused by alkaline gradient mutation, highlight the risk of rapid failure of the weed suppression function, make up for the insufficiency of relying solely on the amount of cumulative damage to reflect sudden degradation, and improve the sensitivity of degradation factors to the identification of critical failure states.

[0047] The calculated cumulative damage amount and the mutation penalty term are numerically superimposed, and the total value after superposition is normalized in the interval [0, 1] to obtain the chemical integrity degradation factor used to characterize the rate of loss of weed suppression function in the base layer. The superposition of cumulative damage amount and mutation penalty term can achieve a comprehensive characterization of gradual and sudden degradation of the base layer, while the normalization in the interval [0, 1] can eliminate the differences in dimensions and numerical magnitudes, making the degradation factor have uniform comparability, which is convenient for subsequent early warning level determination and multi-parameter collaborative analysis, and meets the quantitative assessment needs in practical engineering applications.

[0048] It is worth noting that the mutation penalty term is generated according to the following steps: The critical pH failure value of the surface functional degradation zone of the lime-modified base course under the critical failure state of weed suppression, and the pH reference reserve value of the central alkali reserve zone at the same time point are obtained. The ratio of the critical pH failure value to the pH reference reserve value is used as the preset critical gradient ratio. The current ratio of the first pH value to the second pH value is calculated in real time. If the current ratio is lower than the preset critical gradient ratio, the mutation penalty mechanism is triggered. The deviation of the current ratio from the preset critical gradient ratio is calculated. According to the preset nonlinear mapping relationship, the deviation is converted into an initial penalty value. The nonlinear mapping relationship causes the penalty value to increase at an accelerated rate as the deviation increases. The vertical distance between the surface functional degradation zone and the central alkali reserve zone is obtained. The vertical distance is compared with a preset minimum spacing threshold. When the vertical distance is greater than the preset minimum spacing threshold, a distance correction coefficient is generated to amplify and correct the initial penalty value. The decay acceleration of the first pH value within the current monitoring period is obtained. When the decay acceleration exceeds a preset acceleration threshold, an acceleration correction factor is generated to amplify the penalty value a second time. The penalty value after distance correction and acceleration correction is output as the mutation penalty term. The preset acceleration threshold is set based on engineering experience, and is preferably 0.02 pH / a. 2 -0.1 pH / a 2 When the decay acceleration at the first pH value exceeds a preset acceleration threshold, an acceleration correction factor is generated, calculated using the following formula: ; In the formula, α(a) is the acceleration correction factor, which is dimensionless; a is the decay acceleration at the first pH value, in units of pH / a. 2 ;a th Preset acceleration threshold, unit: pH / a 2 β is the acceleration correction factor, with a value ranging from 0.5 to 2.0.

[0049] In this embodiment, the pH critical failure value refers to the minimum pH limit when the surface functional degradation zone of the lime-modified base layer loses its weed-suppressing function, calibrated by indoor weed-suppressing performance tests, and is dimensionless; the pH reference reserve value refers to the pH reference value corresponding to the central alkali reserve zone at the same moment when the lime-modified base layer is in a critical failure state of weed suppression function, and is dimensionless; the preset critical gradient ratio refers to the ratio of the pH critical failure value to the pH reference reserve value, serving as the threshold for determining whether the weed suppression function has entered a rapid failure stage, and is dimensionless; the current ratio refers to the instantaneous ratio of the first pH value to the second pH value obtained in real time, and is dimensionless; the mutation penalty mechanism refers to the nonlinear penalty calculation process initiated when the pH ratio is lower than the critical gradient, and is dimensionless; the deviation amplitude refers to the absolute value of the difference between the current ratio and the preset critical gradient ratio, used to characterize the degree of degradation deviating from the critical state, and is dimensionless; the initial penalty value refers to the basic penalty value obtained by nonlinear mapping from the deviation amplitude, and is dimensionless; the nonlinear mapping relationship refers to the initial penalty value increasing with the deviation amplitude. The exponential or quadratic accelerating function relationship is used to enhance the sensitivity of identifying severely deteriorated states; it is dimensionless. Vertical distance refers to the actual vertical distance between the first and second pH sensors, measured in millimeters (mm). The preset minimum spacing threshold refers to the minimum vertical distance set to ensure the pH gradient is measurable; it is also measured in millimeters (mm). The distance correction coefficient is an amplification factor generated based on the relative magnitude of the vertical distance and the minimum spacing threshold; the larger the distance, the larger the coefficient; it is dimensionless. The first pH value decay acceleration refers to the rate of change of the first pH value decay rate over time, used to characterize the accelerating trend of deterioration; it is dimensionless. The preset acceleration threshold refers to the critical acceleration value used to determine if deterioration has entered the accelerated stage; it is dimensionless. The acceleration correction factor is a secondary amplification factor introduced when the decay acceleration exceeds the limit, used to intensify the penalty for rapid deterioration; it is dimensionless. The mutation penalty term refers to the final penalty value after distance and acceleration corrections, used to characterize the degree of rapid failure of the weed-suppressing function caused by a mutation in the alkaline gradient; it is dimensionless.

[0050] The specific implementation process is as follows: The mutation penalty term is generated according to the following steps: First, the critical pH failure value (i.e., pH critical failure value) corresponding to the surface functional deterioration zone of the lime-modified base layer under the critical failure state of its weed-suppressing function is obtained. Simultaneously, the pH reference reserve value of the central alkali reserve zone is collected at the same time. The ratio of these two values ​​is used to calculate the preset critical gradient ratio, which serves as the basis for subsequent judgment. Then, the first pH value and the second pH value are collected in real time, and their current ratio is calculated and compared with the preset critical gradient ratio. When the current ratio is lower than the preset critical gradient ratio, the system automatically triggers the mutation penalty mechanism and starts the mutation penalty term generation process.

[0051] First, the deviation of the current ratio from the preset critical gradient ratio is calculated. Then, based on a preset nonlinear mapping relationship, the deviation is converted into an initial penalty value, ensuring that the initial penalty value increases at an accelerating rate with the deviation, highlighting the severity of the penalty under severe degradation conditions. Simultaneously, the actual vertical distance between the surface functional degradation zone and the central alkali reserve zone is acquired and compared with a preset minimum spacing threshold. If the vertical distance is greater than the preset minimum spacing threshold, a distance correction coefficient is generated to amplify and correct the initial penalty value. Simultaneously, the current volumetric moisture content data of the base layer is acquired, and combined with the decay acceleration of the first pH value, an acceleration correction factor is generated to perform a secondary correction on the distance-corrected penalty value, ensuring that the penalty value matches the actual working conditions of the base layer.

[0052] Based on this, the decay acceleration of the first pH value within the current monitoring period is obtained and compared with a preset acceleration threshold. If the decay acceleration exceeds the preset threshold, the penalty value is further amplified to ensure that the mutation penalty term accurately reflects the severity of alkalinity decay at the base layer. Finally, the penalty value after double correction is used as the mutation penalty term and output to the chemical integrity degradation factor calculation stage. This provides reliable support for the accurate calculation of the subsequent chemical integrity degradation factor and provides data for subsequent alkali replenishment maintenance, avoiding inaccurate degradation assessments due to penalty value deviations. The formula for generating the mutation penalty term is as follows: ; ; In the formula, P mut γ(d) is the mutation penalty term, dimensionless; f(x) is a nonlinear mapping function based on pH ratio; γ(d) is the distance correction coefficient, dimensionless; d is the vertical distance between the surface and the middle layer, in mm; th The preset minimum spacing threshold is in mm; η is the distance sensitivity coefficient, ranging from 0.01 to 0.1.

[0053] Furthermore, based on the correlation and fluctuation characteristics of volumetric water content and mechanical response values ​​within the same time window, a hydraulic integrity degradation factor characterizing the degree of water stability damage to the foundation layer is constructed, including: Within the same preset sliding time window, the time series of volumetric water content and the time series of mechanical response values ​​are obtained, and their Pearson correlation coefficient is calculated as the basic component of the associated fluctuation characteristics. The ratio of the peak mechanical response under each vehicle load to the residual deformation after unloading is extracted within the preset sliding time window, and the average value of this ratio within the preset sliding time window is used as the single-load plastic damage characteristic value. Based on the current value of volumetric water content, the softening coefficient of water content on the base layer strength is determined through a preset nonlinear mapping relationship. The nonlinear mapping relationship satisfies the following: the softening coefficient increases at an accelerating rate as the volumetric water content deviates from the optimum water content. The absolute value of the Pearson correlation coefficient, the single-load plastic damage characteristic value, and the softening coefficient are multiplied, and the product result is normalized to obtain the hydraulic integrity degradation factor within the 0-1 interval.

[0054] In this embodiment, volumetric moisture content refers to the proportion of water per unit volume within the lime-modified base course, expressed as a percentage (%); mechanical response value refers to the strain, deflection, or stress values ​​generated by the base course under vehicle load, expressed as microstrain (με) or millimeters (mm); the same time window refers to a preset sliding time window with the same length and step size as the calculation of the chemical integrity degradation factor, ensuring time alignment, expressed as minutes (min) or hours (h); and correlated fluctuation characteristics refer to the synchronicity, coupling, and damage correlation between changes in volumetric moisture content and changes in mechanical response. Regularity, dimensionless; Hydraulic integrity deterioration factor is a quantitative indicator characterizing the degree of water stability damage caused by moisture intrusion, softening, and plastic deformation in lime-modified base courses, with a value range of [0, 1]. A larger value indicates more severe damage, dimensionless; Time series refers to a set of continuous monitoring data arranged chronologically by collection time, dimensionless; Pearson correlation coefficient refers to the degree of linear correlation between the volumetric moisture content time series and the mechanical response value time series, with a value range of [-1, 1], dimensionless; Basic component refers to the fundamental characteristic of the correlation between moisture and mechanical response, characterized by the Pearson correlation coefficient, dimensionless. The term "mechanical response peak" refers to the maximum mechanical response under a single vehicle load, measured in microstrain (με) or millimeters (mm); "residual deformation after unloading" refers to the permanent deformation that cannot be recovered after the load is removed, measured in microstrain (με) or millimeters (mm); "single-load plastic damage characteristic value" refers to the average ratio of the peak mechanical response to the residual deformation within a preset sliding time window, reflecting the degree of irreversible plastic damage caused by the load, and is dimensionless; "optimal moisture content" refers to the moisture content of the lime-modified base layer when it reaches maximum density and mechanical strength, measured in percentage (%); "softening coefficient" refers to the volumetric water content. The softening coefficient, which weakens the mechanical strength of the base layer, increases at an accelerating rate as the moisture content deviates from the optimal value. It is dimensionless. The nonlinear mapping relationship refers to the exponential or quadratic function relationship in which the softening coefficient increases with the deviation of the moisture content. It is used to enhance the sensitivity to strength degradation under high moisture content. It is dimensionless. The product operation refers to the mathematical operation of multiplying the absolute value of the Pearson correlation coefficient, the characteristic value of plastic damage under a single load, and the softening coefficient in sequence. It is dimensionless. The normalization process refers to the standardization process of mapping the product result to the interval [0, 1] to eliminate differences in dimensions and magnitudes and ensure that the indicators are uniform and comparable.

[0055] Specific implementation process: Based on the correlation fluctuation characteristics of volumetric water content and mechanical response values ​​within the same time window, a hydraulic integrity degradation factor characterizing the degree of water stability damage in the foundation layer is constructed. First, the time series formed by volumetric water content and the time series formed by mechanical response values ​​are obtained within the same preset sliding time window as the chemical integrity degradation factor calculation. These two sets of time series data are substituted into the Pearson correlation coefficient calculation formula to obtain the Pearson correlation coefficient, which is then used as the basic component of the correlation fluctuation characteristics. The formula for calculating the Pearson correlation coefficient r is: ; In the formula, x i Let y be the volumetric water content at the i-th sampling time, dimensionless; i The mechanical response value at the i-th sampling time, in mm; This is the average volumetric moisture content, dimensionless. is the mean value of the mechanical response, in mm; n is the total number of sampling points, dimensionless.

[0056] The product result S is normalized and mapped to the interval [0,1], as shown in the following formula: ; In the formula, S norm S is the normalized hydraulic integrity degradation factor, dimensionless; S is the unnormalized comprehensive damage product, dimensionless; S min S is the product of the historical minimum damage, dimensionless; max This is the product of the historical maximum damage, which is dimensionless.

[0057] By selecting a sliding time window consistent with the calculation of the chemical integrity degradation factor, the temporal dimension of the two sets of degradation factors can be kept consistent. By calculating the Pearson correlation coefficient, the linear correlation between volumetric water content and mechanical response value can be accurately quantified, providing a reliable basis for the correlation fluctuation characteristics and avoiding the deviation in water stability damage assessment caused by data asynchrony and inaccurate correlation quantification.

[0058] Within the same preset sliding time window, the peak mechanical response and residual deformation after unloading are extracted successively for each vehicle load. The ratio of the peak mechanical response to the residual deformation is calculated, and the arithmetic mean of all single ratios within the window is taken as the characteristic value of plastic damage under a single load. Extracting the peak value and residual deformation under each load and averaging the ratios effectively avoids errors caused by fluctuations in a single load, accurately quantifies the cumulative degree of plastic damage in the base layer under repeated vehicle loads, provides load-related core characteristic indicators for water-stable damage assessment, and ensures the specificity of damage characterization.

[0059] Based on the current measured value of volumetric moisture content, the softening coefficient of moisture content on the strength of the base course is calculated through a preset nonlinear mapping relationship. This nonlinear mapping relationship satisfies the following condition: the greater the deviation of the volumetric moisture content from the optimum moisture content, the faster the softening coefficient increases, highlighting the strength degradation effect caused by abnormally high moisture content. Using this nonlinear mapping relationship to calculate the softening coefficient accurately reflects the nonlinear influence of moisture on the strength of the base course, emphasizing the risk of strength weakening caused by abnormal deviations in moisture content, avoiding the problem that linear calculations cannot reflect the exacerbation of damage under extreme moisture content, and improving the accuracy of water stability damage assessment.

[0060] The absolute value of the Pearson correlation coefficient, the characteristic value of plastic damage under single load, and the softening coefficient are multiplied together to obtain the comprehensive damage product. This product operation enables the synergistic coupling of moisture-related effects, load-induced plastic damage, and strength softening effects, comprehensively integrating the core influencing factors of water-stable damage. This avoids the one-sidedness of assessments based on single characteristic indicators and ensures that the comprehensive damage result fully reflects the true state of water stability in the base course.

[0061] The damage product results are normalized and mapped to the [0,1] interval to obtain the hydraulic integrity degradation factor with a value in the 0-1 interval. Normalization to the [0,1] interval effectively eliminates the differences in dimensions and numerical magnitudes of various characteristic indicators, ensuring the hydraulic integrity degradation factor has uniform comparability. It also maintains consistency with the value range of the previous hydraulic integrity degradation factor, facilitating their collaborative participation in early warning level determination and multi-parameter comprehensive analysis, thus meeting the application needs of practical engineering quantitative assessment.

[0062] Understandably, step S2 focuses on two core failure mechanisms during the long-term service of lime-modified base courses: alkaline degradation and moisture load coupled damage. It quantitatively constructs dual-dimensional degradation evaluation indicators for each mechanism. Based on the temporal decay differences and gradient abrupt change characteristics of pH monitoring data at different vertical depths, and superimposed with multiple composite corrections based on distance conditions and degradation acceleration characteristics, it comprehensively quantifies the asynchronous alkaline loss and critical functional failure risk, accurately characterizing the overall loss rate of the base course's weed-suppressing function. Simultaneously, it combines the temporal correlation between volumetric moisture content and mechanical response parameters within the same time window, and vehicle load... The cumulative effect of plastic deformation caused by reciprocating load and the nonlinear softening effect of material strength caused by moisture content shift comprehensively couple the synergistic damage effects of moisture migration, load disturbance and material weakening. Through the fusion and superposition of multiple characteristic components and unified normalization processing, chemical integrity degradation factors and hydraulic integrity degradation factors with clear physical meaning and uniform and comparable intervals are formed respectively. This enables the simultaneous quantitative assessment of the chemical durability and hydraulic stability of lime-modified base courses, providing continuous and reliable quantitative index support for subsequent determination of the overall health status of the base course, degradation classification and formulation of long-term maintenance strategies.

[0063] S3: Compare the chemical integrity degradation factor and hydraulic integrity degradation factor with the preset multi-level early warning threshold range, respectively. At the same time, compare the first pH value, the second pH value, the volumetric water content and the mechanical response value with the corresponding single-parameter critical threshold, respectively. Determine the early warning level according to the combination of comparison results.

[0064] In this embodiment, the multi-level early warning threshold range refers to multiple numerical ranges pre-divided based on long-term service test data of lime-modified base course, material deterioration patterns, and road engineering maintenance standards, corresponding to different risk levels such as normal working conditions, slight deterioration, moderate damage, and severe failure; the single-parameter critical threshold refers to the safety critical limit set separately for the first pH value, the second pH value, the volumetric moisture content, and the mechanical response value, used to determine whether a single monitoring parameter exceeds the allowable range for normal service, and the threshold values ​​of each parameter are matched with the corresponding physical dimensions; the combined state refers to the comprehensive judgment working condition formed by the superposition and coupling of the judgment results of the dual deterioration factor range and the judgment results of multiple single parameters exceeding the limit; the early warning level refers to the standardized risk level divided according to the multi-indicator joint judgment rule, used to intuitively distinguish the current degree of deterioration risk and the degree of maintenance urgency of the lime-modified base course.

[0065] The first pH threshold is set at 9.5~12.5 to define the range of surface alkaline environment; the second pH threshold is set at 10.5~13.0 to define the stability range of deep cementitious materials; the volumetric moisture content threshold is set at 5%~25% to define the optimal moisture content fluctuation range of the base layer, avoiding excessive moisture softening or excessive dryness cracking; and the mechanical response value threshold is set at 400 microstrain (με) (or the displacement response value threshold is set at 2.0 mm) to define the elastic limit of the base layer structure.

[0066] The specific implementation process is as follows: Based on the multi-indicator linkage judgment logic, a comprehensive early warning system for the health status of the grassroots level is constructed. First, the pre-calibrated and solidified multi-level early warning threshold range is retrieved. The chemical integrity deterioration factor and hydraulic integrity deterioration factor calculated in real time are substituted into the corresponding interval comparison logic to accurately determine the deterioration level range of each of the two types of factors and clarify the current status of the grassroots chemical characteristics and water stability. Simultaneously, the single-parameter critical threshold corresponding to each monitoring parameter is retrieved. The first pH value, second pH value, volumetric water content, and mechanical response value collected in real time are compared with their respective matched single-parameter critical thresholds one by one to independently determine whether each basic monitoring parameter has problems such as exceeding the limit or abnormal operating conditions, and accurately identify the abnormal situation of a single parameter.

[0067] Subsequently, the interval assignment results of the two types of deterioration factors and the over-limit judgment results of each single parameter are fully summarized to form a comprehensive comparison and combination of multi-dimensional parameters, ensuring that all judgment results are complete and without deviation. Based on the preset multi-indicator linkage judgment rules, the summarized comparison results are graded and matched. Combining chemical deterioration, hydraulic damage, single parameter anomalies and other aspects, the health status of lime-modified base course is comprehensively judged. Finally, standardized warning levels are automatically classified and output, realizing joint warning judgment of multiple dimensions such as chemical integrity, hydraulic damage, and single parameter anomalies. This avoids the limitations of single indicator judgment, ensures the comprehensiveness and accuracy of warning results, and provides a reliable judgment basis for subsequent maintenance decisions.

[0068] Understandably, this step abandons the limitations of independent judgment based on a single parameter, adopting a judgment logic that combines a comprehensive deterioration factor with in-situ measured parameters in a two-layer linkage comparison. This takes into account both the long-term, gradual deterioration pattern of lime-modified base courses and the abnormal characteristics of instantaneous parameter mutations. By dividing the degree of deterioration into multi-level threshold intervals, it achieves a refined classification of the degree of deterioration. Combined with the simultaneous verification of multiple key indicators, it avoids the problems of missed judgments and misjudgments caused by relying on a single indicator. By relying on the method of matching early warning levels with multiple result combinations, it comprehensively covers various disease-inducing scenarios such as alkaline decay, water-stable damage, abnormal moisture, and excessive load response. It can objectively and comprehensively reflect the comprehensive service risk of lime-modified base courses, providing a standardized classification basis for early detection, early warning, and differentiated maintenance and treatment of road base course diseases.

[0069] S4: Based on the time-series evolution trajectory of chemical integrity degradation factors within historical cycles, a time-series extrapolation algorithm is used to predict the remaining time before the weed suppression function reaches critical failure. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value. The basic alkali replenishment amount is then corrected using the current volumetric moisture content of the base layer, and an early warning instruction carrying the recommended alkali replenishment amount information is generated.

[0070] The process of predicting the remaining time to reach the critical failure of weed suppression function based on the temporal evolution trajectory of chemical integrity degradation factors within a historical period using a temporal extrapolation algorithm includes: obtaining the temporal sequence of chemical integrity degradation factors within a preset historical period, and the triggering status record of the mutation penalty mechanism at each time point; the triggering status record is used to identify whether the mutation penalty mechanism has been triggered at each time point; according to the triggering status record, the temporal sequence is divided into a gradual phase where mutation penalty has not been triggered and an accelerated phase where mutation penalty has been triggered; the chemical integrity degradation factors in the accelerated phase include mutation penalty terms, while the chemical integrity degradation factors in the gradual phase do not include mutation penalty terms; for the chemical integrity degradation factor sequence in the gradual phase, the first decay model is used to fit its evolution trajectory; for the chemical integrity degradation factor sequence in the accelerated phase... The degradation factor sequence is fitted with a second decay model to determine its evolution trajectory. The decay rate of the second decay model is greater than that of the first decay model. Based on the segmented combination of the first and second decay models, the chemical integrity degradation factor value at future times is extrapolated and predicted. During the extrapolation process, when the current ratio of the first pH value to the second pH value at a future time is predicted to be lower than the preset critical gradient ratio, the prediction model is switched from the first decay model to the second decay model at that future time, and a mutation penalty term is superimposed on the predicted value after the switch. The predicted chemical integrity degradation factor value is compared with the preset critical failure threshold of the herbicide suppression function to determine the moment when the predicted value first reaches the critical failure threshold of the herbicide suppression function. The time difference between this moment and the current moment is calculated as the remaining time.

[0071] In this application embodiment, the preset historical period refers to a fixed time length used to trace the deterioration evolution law of lime-modified base course and continuously backtrack and statistically analyze it, with the unit being days (d); the time-series evolution trajectory refers to the dynamic change curve formed by the continuous change of chemical integrity deterioration factor with monitoring time, which is dimensionless; the time-series extrapolation algorithm refers to a numerical prediction algorithm that fits the evolution law based on historical deterioration time-series data and extrapolates the future deterioration trend; the mutation penalty mechanism triggering state record refers to the time-series identification data stored synchronously throughout the process, used to mark whether each monitoring moment meets the pH gradient exceeding the limit condition and start the mutation penalty calculation, which is dimensionless; the gradual stage refers to the stable deterioration stage in which the mutation penalty mechanism is not triggered throughout the process and is dominated only by the slow loss of alkalinity, and the chemical integrity deterioration factor corresponding to this stage only includes the cumulative damage amount and does not superimpose the mutation penalty term; the accelerated stage refers to the mutation deterioration stage in which the mutation penalty mechanism has been triggered and the alkalinity gradient imbalance has caused rapid deterioration, and the chemical integrity deterioration factor corresponding to this stage is dimensionless. The degradation factor includes both cumulative damage and abrupt penalty term; the first degradation model refers to a stable degradation fitting model constructed to adapt to the slow degradation law of the gradual stage, and the overall degradation rate of the model is relatively low; the second degradation model refers to an accelerated degradation fitting model constructed to adapt to the rapid degradation law of the accelerated stage, and the overall degradation rate of the second degradation model is always greater than that of the first degradation model; segmented combination prediction refers to a prediction method that matches the corresponding degradation model to carry out joint extrapolation according to the temporal segmentation characteristics of the gradual stage and the accelerated stage; the model switching condition refers to the model switching judgment condition when the predicted ratio of the first pH value to the second pH value at a future time is lower than the preset critical gradient ratio during the extrapolation process; the critical failure threshold of the weed suppression function refers to the critical value of the chemical integrity degradation factor that determines the complete failure of the weed suppression function of lime-modified base course, which is dimensionless; the remaining time refers to the time interval from the current monitoring time to the chemical integrity degradation factor reaching the critical failure threshold of the weed suppression function, in days (d).

[0072] The specific implementation process is as follows: First, the time series sequence of chemical integrity degradation factors continuously collected and stored within a preset historical period is retrieved. Simultaneously, the trigger status records of the mutation penalty mechanism at each monitoring moment are retrieved. These trigger status records can accurately identify whether each time series node meets the gradient over-limit condition and whether the mutation penalty mechanism operation is initiated. Based on the trigger status records of the mutation penalty mechanism, the complete time series sequence of chemical integrity degradation factors is strictly divided into two independent evolution stages: a gradual change stage where the mutation penalty mechanism is not triggered throughout the entire process, and an acceleration stage where the mutation penalty mechanism is continuously or intermittently triggered. The chemical integrity degradation factor value corresponding to the acceleration stage is superimposed with a mutation penalty term, while the chemical integrity degradation factor corresponding to the gradual change stage is calculated only from the cumulative damage amount and does not contain the interference of the mutation penalty term. For the time series data of chemical integrity degradation factors corresponding to the gradual change stage, a first decay model adapted to the slow alkaline decay law is selected for nonlinear fitting to accurately restore the time series evolution trajectory of the stable degradation stage. The first decay model adopts a linear decay model, and the formula is as follows: ; In the formula, D1(t) is the predicted value of the chemical integrity degradation factor in the gradual change stage, which is dimensionless; k is the linear decay coefficient in the gradual change stage, with the unit being 1 / a; b is a constant term, which is dimensionless; and t is time, with the unit being a.

[0073] For the time-series data of chemical integrity degradation factors corresponding to the accelerated phase, a second decay model adapted to the alkaline gradient imbalance and accelerated degradation development law was selected for trajectory fitting. The second decay model was set to a high decay rate mode to ensure matching with the actual rapid degradation characteristics. The second decay model adopts an exponentially accelerated decay model, and the formula is as follows: ; In the formula, D2(t) is the predicted value of the chemical integrity degradation factor in the accelerated stage, which is dimensionless; D0 is the initial degradation degree in the accelerated stage, which is dimensionless; λ is the accelerated decay factor, λ>0, with the unit: 1 / a.

[0074] After completing the two-stage model fitting, a segmented time-series prediction framework is constructed by combining the first and second decay models. Based on this framework, the predicted values ​​of the chemical integrity degradation factor at different future times are calculated step by step. During the continuous time-series extrapolation calculation, the changing patterns of the first and second pH values ​​at future times are simultaneously extrapolated, and their ratio is calculated in real time. When the predicted pH ratio at any future time is lower than the preset critical gradient ratio, the prediction model is automatically switched at that time point, from the original first decay model to the second decay model. Simultaneously, during the model switching... The corresponding level of mutation penalty term is synchronously superimposed on all the prediction results after the replacement to restore the accelerated evolution characteristics after the deterioration mutation; the predicted value of chemical integrity deterioration factor obtained by long time series extrapolation is continuously output, and all predicted values ​​are compared with the preset critical failure threshold of the weed suppression function point by point to lock the target time when the predicted value first stably reaches the critical failure threshold of the weed suppression function; the time difference between the target failure time and the current actual monitoring time is accurately calculated, and the time difference is uniformly defined as the remaining time of the critical failure of the weed suppression function of the lime modified base layer, thus completing the quantitative prediction of the duration based on the time series extrapolation algorithm.

[0075] Furthermore, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target restored pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the substrate. The generation of an early warning instruction carrying recommended alkali replenishment information includes: obtaining the remaining time; triggering the early warning instruction generation process when the remaining time is less than a preset maintenance window threshold; calculating the difference between the second pH value and the preset target restored pH value; determining the basic alkali replenishment amount based on a preset mapping relationship of alkali replenishment amount required per unit pH difference; obtaining the current volumetric moisture content of the substrate; and determining the amount corresponding to the volumetric moisture content based on a preset moisture content correction mapping relationship. The diffusion loss correction coefficient is calculated; the diffusion loss correction coefficient increases non-linearly with the increase of volumetric water content; the basic alkali replenishment amount is multiplied by the diffusion loss correction coefficient to obtain the final recommended alkali replenishment amount; the current hydraulic integrity degradation factor is obtained, and when the hydraulic integrity degradation factor exceeds the preset hydraulic warning threshold, the base structure reinforcement suggestion is added to the warning instruction; the maintenance urgency level is determined according to the ratio of the remaining time to the preset maintenance window threshold; the smaller the ratio, the higher the maintenance urgency level; and a warning instruction carrying the final recommended alkali replenishment amount, maintenance urgency level, and base structure reinforcement suggestion is generated.

[0076] In this embodiment, the preset maintenance window threshold refers to the pre-defined critical duration of preventive maintenance at the grassroots level, expressed in days (d), used to determine whether immediate intervention in alkali replenishment is needed. Typically, the preset maintenance window threshold ranges from 30 to 90 days. The target recovery pH value refers to the optimal pH recovery target value for the central alkali reserve area, preset to restore the alkali reserve level of the lime-modified grassroots and ensure long-term weed suppression function; it is dimensionless. The mapping relationship of alkali replenishment required per unit pH difference refers to the quantitative correspondence between the pH difference and the amount of alkali replenishment material per unit area, calibrated through indoor mixing experiments. The basic alkali replenishment amount refers to the baseline amount of alkali replenishment material calculated only based on the acid-base difference, expressed in kilograms per square meter (kg / m²). 2 The moisture content correction mapping relationship refers to the quantitative functional relationship characterizing the influence of the base layer moisture content on the penetration, loss, and diffusion loss of alkali-adding agents; the diffusion loss correction coefficient is a dimensionless parameter used to correct for the migration and seepage loss of alkali-adding materials with pore moisture under high moisture content conditions, and the diffusion loss correction coefficient shows a non-linear growth law with increasing volumetric moisture content; the final recommended alkali-adding amount refers to the actual recommended alkali-adding amount obtained after moisture content diffusion loss correction, and the unit is kilograms per square meter (kg / m²). 2 The hydraulic warning threshold refers to the critical value of the hydraulic integrity deterioration factor that determines whether the water stability damage of the base layer exceeds the standard and the structural deformation resistance decreases. It is dimensionless. The maintenance urgency level refers to the maintenance priority classification index based on the remaining failure time, which is used to differentiate the on-site maintenance operation sequence. The early warning instruction refers to the standardized push instruction text that integrates and quantifies maintenance parameters, risk level, and reinforcement suggestions.

[0077] The specific implementation process is as follows: Based on the remaining time of the predicted critical failure of the weed suppression function, a pre-judgment process for generating early warning commands is initiated. First, the remaining time data is retrieved, and simultaneously, a preset maintenance window threshold is retrieved. These two are precisely compared. Only when the remaining time is less than the preset maintenance window threshold will the system automatically trigger the complete generation process of the early warning command. The advantage of this setting is that by using the preset maintenance window threshold as a trigger threshold, it avoids resource waste caused by prematurely initiating maintenance warnings and also prevents delayed warnings from missing the optimal maintenance opportunity, achieving accuracy and rationality in early warning triggering and ensuring timely and economical maintenance intervention.

[0078] After the early warning command generation process is triggered, the second pH value monitored by the second pH sensor deployed in the central alkali reserve area is read in real time to accurately obtain the current actual state of alkali reserves within the base layer. Then, the difference between this second pH value and the preset target recovery pH value is calculated to determine the specific degree of alkali deficit in the base layer, obtaining a precise alkali deficit difference value. Based on this, relying on the mapping relationship of alkali replenishment required per unit pH difference, which has been determined and solidified through indoor experiments and on-site calibration, the basic alkali replenishment amount for neutralizing acid-base deficits, restoring the base layer alkali reserve level, and ensuring the long-term effectiveness of weed suppression is quantitatively matched and calculated according to the calculated alkali deficit difference value. The advantages of this setup are that it uses the pH value of the central alkali reserve area as the core calculation basis, closely matches the actual distribution characteristics of the base layer alkali reserves, and the preset mapping relationship, calibrated through experiments, ensures the accuracy of the basic alkali replenishment calculation, avoiding material waste and environmental impact caused by insufficient alkali replenishment failing to restore weed suppression function or excessive alkali replenishment.

[0079] While calculating the basic alkali supplementation amount, the measured volumetric moisture content of the lime-modified base layer is simultaneously collected to comprehensively understand the current moisture distribution of the base layer. Then, a preset moisture content correction mapping relationship is retrieved, and combined with the currently measured volumetric moisture content value, the corresponding diffusion loss correction coefficient is obtained. This moisture content correction mapping relationship strictly follows the law that the higher the volumetric moisture content, the more significant the loss of alkali supplementation agent with water diffusion, and the diffusion loss correction coefficient increases non-linearly, ensuring a high degree of fit between the correction coefficient and the on-site moisture conditions. The advantage of this setting is that it fully considers the impact of base layer moisture on the diffusion loss of alkali supplementation agents, and compensates for the agent loss deviation caused by water migration through a non-linear correction coefficient. This avoids problems such as insufficient alkali supplementation and ineffective recovery of weed suppression function due to ignoring the influence of moisture, thus improving the on-site adaptability of the alkali supplementation scheme.

[0080] The calculated baseline alkali replenishment amount is multiplied by the obtained diffusion loss correction coefficient. This calculation eliminates the deviation caused by water migration leading to alkali replenishment agent loss, accurately determining the final recommended alkali replenishment amount that adapts to the actual on-site water conditions and ensures the effectiveness of alkali replenishment. The advantage of this setup is that through dual quantitative calculation and correction, dynamic adaptation of the alkali replenishment amount is achieved. This ensures that the replenishment amount meets the needs of restoring alkali reserves and maintaining weed suppression function, while avoiding cost waste caused by excessive alkali replenishment, thus balancing the effectiveness and economy of alkali replenishment.

[0081] While determining the final recommended amount of alkali replenishment, the hydraulic integrity degradation factor calculated in real time is simultaneously retrieved and compared with a preset hydraulic warning threshold. When the hydraulic integrity degradation factor exceeds the preset hydraulic warning threshold, the risk of continuous water stability damage and material softening in the base layer is immediately identified. At this point, corresponding base layer structural reinforcement suggestions are simultaneously added to the warning instruction, achieving coordinated guidance for alkali replenishment maintenance and structural reinforcement. The advantage of this setting is that it takes into account both the restoration of the base layer's weed suppression function and the prevention and control of water stability damage, avoiding focusing solely on alkali replenishment while ignoring the overall failure of the base layer caused by structural damage. It achieves multi-dimensional maintenance guidance, improves the comprehensiveness and practicality of the warning instruction, and provides comprehensive technical support for on-site maintenance.

[0082] To clearly define the urgency of on-site maintenance and accurately schedule maintenance, the ratio of remaining time to a preset maintenance window threshold is calculated. Based on this ratio, maintenance urgency levels are categorized, generally following the logic that the smaller the ratio, the shorter the remaining maintenance window, and the higher the maintenance urgency level. This achieves a quantitative classification of maintenance urgency. The advantage of this system is that by quantifying urgency levels, it directly reflects the urgency of maintenance tasks, allowing maintenance personnel to prioritize high-urgency frontline maintenance work, rationally allocate maintenance resources, and improve the efficiency and focus of maintenance work.

[0083] Finally, the calculated recommended alkali replenishment amount, the stratified maintenance urgency levels, and the additional base structure reinforcement recommendations (added when the hydraulic integrity deterioration factor exceeds the limit) are uniformly packaged and integrated to generate and output standardized early warning instructions that are complete in content, clear in parameters, logically sound, and can directly guide on-site maintenance operations. The advantage of this setup is that it integrates all core maintenance information into standardized and regulated early warning instructions, avoiding on-site operational confusion caused by scattered maintenance parameters. It ensures that maintenance personnel can quickly obtain key maintenance information, accurately implement maintenance measures such as alkali replenishment and reinforcement, and improve the standardization and efficiency of maintenance operations.

[0084] Understandably, step S4 relies on long-term monitoring data of chemical integrity degradation factors, combined with trigger records of mutation penalty mechanisms to divide the gradual degradation stage into accelerated degradation stages. It uses a segmented differential decay model to fit the degradation evolution law and combines it with a dynamic switching prediction model based on pH gradient mutation conditions to achieve accurate time-series extrapolation prediction of the remaining time of critical failure of the grass-suppressing function of lime-modified base layers. When the remaining maintenance time is predicted to be less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the measured pH index in the central alkali reserve area and the target recovery pH value. Simultaneously, a nonlinear diffusion loss correction coefficient is introduced in conjunction with the real-time volumetric moisture content of the base layer. The system achieves dynamic correction of alkali replenishment amount, forming a final recommended alkali replenishment amount that fits the on-site moisture conditions. Simultaneously, it couples hydraulic integrity deterioration factors to conduct water stability damage verification, adds base structure reinforcement suggestions as needed, and quantifies the maintenance urgency level based on the ratio of remaining time to maintenance window threshold. Finally, it integrates multiple key maintenance information to generate an integrated early warning command. This system not only achieves early prediction of the alkaline deterioration life of the base layer and quantitative guidance for proactive alkali replenishment maintenance, but also takes into account the risk warning of moisture-induced structural synergistic damage. It constructs a long-term preventive maintenance decision-making system for lime-modified base layers that integrates deterioration prediction, dosage correction, risk classification, and intelligent early warning.

[0085] In summary, steps S1 to S4 construct a complete system for health monitoring, degradation assessment, risk warning, and preventive maintenance decision-making in lime-modified base courses, forming a closed-loop process from data collection to maintenance guidance. Step S1, as a foundational step, involves deploying multi-type sensor arrays along the vertical cross-section of the base course, synchronously collecting surface and internal pH values, volumetric water content, and mechanical response values ​​under vehicle loads according to preset rules. This clarifies the sensor deployment specifications and data collection mode, and scientifically determines the minimum spacing threshold for pH sensors, providing real, synchronous, and multi-dimensional in-situ monitoring data support for subsequent degradation analysis. Step S2 focuses on the two core failure mechanisms of the base course. Based on the temporal decay differences and gradient abrupt changes in pH values, a chemical integrity degradation factor is constructed using multiple composite corrections to quantify the rate of weed suppression function loss. Simultaneously, the correlation fluctuations between volumetric water content and mechanical response, plastic damage, and strength softening effects are coupled to construct a hydraulic integrity degradation factor, achieving chemical resistance degradation of the base course. The system simultaneously quantifies and evaluates the durability and hydraulic stability of the roadbed. Step S3 employs a dual-layer linkage judgment logic, comparing the two degradation factors with multi-level early warning threshold ranges and verifying various basic monitoring parameters with their corresponding single-parameter critical thresholds. It matches the warning level with multiple result combinations to avoid omissions and misjudgments caused by single-indicator judgments, objectively reflecting the comprehensive service risk of the roadbed. Step S4 achieves deep integration of degradation prediction and maintenance guidance. Through a segmented differentiated attenuation model and a time-series extrapolation algorithm, it accurately predicts the remaining time of critical failure of the weed-suppressing function. When the remaining time is insufficient, it determines the basic alkali replenishment amount based on pH difference and corrects for moisture content to obtain the final recommended alkali replenishment amount. Simultaneously, it couples the hydraulic damage risk with additional structural reinforcement suggestions and classifies the maintenance urgency level, generating an integrated early warning command. The entire system is progressive and interconnected, achieving both accurate identification and risk classification of the roadbed degradation state and providing quantifiable and implementable preventative maintenance solutions. This effectively ensures the long-term stable service of lime-modified roadbeds and provides scientific and systematic technical support for the long-term operation and maintenance of roadbeds.

[0086] like Figure 2 As shown in the embodiment of this application, an intelligent monitoring and early warning system for the condition of high-standard farmland road base layers is also provided. The system includes: The data acquisition module is used to acquire sensor array data at different vertical depths within the cross section of the lime-modified base layer, including at least the first pH value located on the surface of the base layer, the second pH value located at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load. The data construction module is used to construct a chemical integrity degradation factor that characterizes the rate of loss of weed suppression function of the base layer based on the relative decay difference between the first pH value and the second pH value in a continuous monitoring period; and to construct a hydraulic integrity degradation factor that characterizes the degree of water stability damage of the base layer based on the correlation fluctuation characteristics of volumetric water content and mechanical response value within the same time window. The data processing module is used to compare the chemical integrity degradation factor and the hydraulic integrity degradation factor with the preset multi-level early warning threshold range, and at the same time compare the first pH value, the second pH value, the volumetric water content and the mechanical response value with the corresponding single-parameter critical threshold, and determine the early warning level according to the combination of comparison results. The data processing module is used to predict the remaining time before the weed suppression function reaches its critical failure based on the time-series evolution trajectory of the chemical integrity degradation factor within the historical cycle and a time-series extrapolation algorithm. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the base layer, generating an early warning instruction carrying the recommended alkali replenishment amount information.

[0087] In this embodiment, the data acquisition module is mainly used to control the sensor array deployed in the vertical cross-sectional partition of the lime-modified base layer, synchronously collecting the first pH value, the second pH value, the volumetric water content, and the mechanical response value under vehicle load (wherein the mechanical response sensor uses event-triggered acquisition), providing real-time and synchronous raw data support for subsequent system processing; the data construction module is mainly used to generate a chemical integrity degradation factor characterizing the rate of weed suppression function loss based on the temporal decay difference and gradient mutation characteristics of the first and second pH values, and to generate a hydraulic integrity degradation factor characterizing the degree of water stability damage based on the correlation fluctuation of volumetric water content and mechanical response value, plastic damage, and strength softening effect; the data processing module is mainly used for The system employs a dual-layer linkage judgment logic, comparing two types of deterioration factors with multi-level early warning thresholds and verifying various original monitoring parameters with corresponding single-parameter critical thresholds. After summarizing and combining the results, the early warning level is determined, intuitively reflecting the comprehensive service risk of the base layer. The data processing module is mainly used to predict the remaining time of critical failure of the base layer's weed suppression function through a segmented attenuation model and a time-series extrapolation algorithm. When the remaining time is less than the preset threshold, the system combines the second pH value and volumetric moisture content to determine and correct the alkali replenishment amount, adds structural reinforcement suggestions as needed, classifies maintenance urgency, and generates standardized early warning instructions. The four modules work together to form a closed-loop process, realizing intelligent monitoring of the entire process of lime-modified base layers, and providing technical support for the long-term operation and maintenance of high-standard farmland road base layers.

[0088] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application. Those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent monitoring and early warning of the condition of high-standard farmland road base layers, characterized in that, include: Data from sensor arrays deployed at different vertical depths within the cross-section of a lime-modified base layer are obtained, including at least the first pH value at the surface of the base layer, the second pH value at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load. Based on the relative attenuation difference between the first pH value and the second pH value during a continuous monitoring period, a chemical integrity degradation factor characterizing the rate of loss of weed suppression function at the grassroots level was constructed. Based on the correlation and fluctuation characteristics of volumetric water content and mechanical response value within the same time window, a hydraulic integrity deterioration factor characterizing the degree of water stability damage in the foundation is constructed. The chemical integrity degradation factor and the hydraulic integrity degradation factor are compared with the preset multi-level early warning threshold range, and the first pH value, the second pH value, the volumetric water content and the mechanical response value are compared with the corresponding single-parameter critical thresholds. The early warning level is determined according to the combination of the comparison results. Based on the temporal evolution trajectory of the chemical integrity deterioration factor within the historical cycle, a temporal extrapolation algorithm is used to predict the remaining time before the weed suppression function reaches critical failure. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the base layer, generating an early warning instruction carrying the recommended alkali replenishment amount information.

2. The method according to claim 1, characterized in that, Methods for acquiring sensor array data deployed at different vertical depths within the cross-section of a lime-modified base course include: Along the vertical cross-section of the lime-modified base course, according to the preset vertical proportion of the total thickness of the base course, it is divided into the surface functional deterioration zone, the middle alkali reserve zone and the bottom hydraulic disturbance zone. A first pH sensor is installed in the surface functional deterioration zone to collect the first pH value; a second pH sensor is installed in the central alkali reserve zone to collect the second pH value; the vertical projections of the first pH sensor and the second pH sensor coincide, and the vertical distance between them is not less than a preset minimum distance threshold, which is set based on the total thickness of the lime-modified base layer. At the same vertical depth as the first pH sensor and the second pH sensor, volumetric moisture content sensors are respectively deployed to collect the volumetric moisture content of the base layer. A mechanical response sensor is installed on the top surface of the base layer at a position that coincides with the vertical projection of the first pH sensor to collect mechanical response values ​​under vehicle load. The system controls each sensor to collect data synchronously according to preset acquisition rules. The mechanical response sensor is awakened and performs data acquisition using an event-triggered method with preset trigger conditions.

3. The method according to claim 2, characterized in that, The preset minimum spacing threshold is obtained through the following steps: The measurement accuracy values ​​of the first pH sensor and the second pH sensor are obtained, as well as the annual average pH decay rate of the lime-modified base layer under preset conditions. The annual average pH decay rate is obtained by using a preset prediction model based on the local annual average rainfall pH value, atmospheric carbon dioxide concentration and base layer lime content. Based on the measurement accuracy value and the preset monitoring period, determine the minimum pH difference value that can be effectively distinguished by the sensor within the preset monitoring period. The minimum pH difference value shall not be less than a preset multiple of the measurement accuracy value. Based on the average annual pH decay rate and the minimum pH difference, calculate the vertical distance required to generate the minimum pH difference between the surface functional deterioration zone and the central alkali reserve zone. Multiply the vertical distance by a preset safety factor to obtain the preset minimum spacing threshold. The preset safety factor is greater than 1.

4. The method according to claim 1, characterized in that, Based on the relative attenuation difference between the first and second pH values ​​over a continuous monitoring period, a chemical integrity degradation factor characterizing the rate of loss of weed-suppressing function at the grassroots level was constructed, including: The difference in decay rate between the first pH value and the second pH value within a preset sliding time window is calculated. The difference in decay rate is used to characterize the asynchronous leaching of alkaline soil between the surface and interior of the base layer. The cumulative damage amount of the alkaline environment at the base layer is obtained by performing a time-dimensional cumulative calculation on the difference in attenuation rate within a preset sliding time window. When the current ratio of the first pH value to the second pH value is lower than the preset critical gradient ratio, a mutation penalty term is generated; the preset critical gradient ratio is determined based on the preset ratio of pH between the surface and interior of the lime-modified base layer under the critical failure state of its weed-suppressing function. The cumulative damage amount is superimposed with the mutation penalty term and then normalized to obtain the chemical integrity degradation factor.

5. The method according to claim 4, characterized in that, The mutation penalty term is generated according to the following steps: The pH critical failure value of the surface functional deterioration zone of lime-modified base course under the critical failure state of weed suppression function, and the pH reference reserve value of the central alkali reserve zone at the same time point are obtained. The ratio of the pH critical failure value to the pH reference reserve value is used as the preset critical gradient ratio. The current ratio of the first pH value to the second pH value is calculated in real time. If the current ratio is lower than the preset critical gradient ratio, a mutation penalty mechanism is triggered. Calculate the deviation of the current ratio from the preset critical gradient ratio, and convert the deviation into an initial penalty value according to the preset nonlinear mapping relationship. The nonlinear mapping relationship makes the penalty value increase at an accelerated rate as the deviation increases. Obtain the vertical distance between the surface functional degradation zone and the central alkali reserve zone, compare the vertical distance with a preset minimum spacing threshold, and when the vertical distance is greater than the preset minimum spacing threshold, generate a distance correction coefficient to amplify and correct the initial penalty value; The decay acceleration of the first pH value within the current monitoring period is obtained. When the decay acceleration exceeds the preset acceleration threshold, an acceleration correction factor is generated to amplify the penalty value a second time. The penalty values ​​after distance and acceleration corrections are output as mutation penalty terms.

6. The method according to claim 1, characterized in that, Based on the correlation and fluctuation characteristics of volumetric water content and mechanical response value within the same time window, a hydraulic integrity deterioration factor characterizing the degree of water stability damage in the foundation layer is constructed, including: Within the same preset sliding time window, obtain the time series of volumetric water content and the time series of mechanical response values, calculate the Pearson correlation coefficient between the two, and use it as the basic component of the associated fluctuation characteristics. Extract the ratio of the peak mechanical response under each vehicle load to the residual deformation after unloading within a preset sliding time window. Take the average value of the comparison value within the preset sliding time window as the characteristic value of plastic damage under a single load. Based on the current value of volumetric moisture content, the softening coefficient of moisture content on the strength of the base layer is determined through a preset nonlinear mapping relationship; the nonlinear mapping relationship satisfies that the softening coefficient increases at an accelerating rate as the volumetric moisture content deviates from the optimum moisture content. The absolute value of the Pearson correlation coefficient, the characteristic value of plastic damage under single load, and the softening coefficient are multiplied together, and the product result is normalized to obtain the hydraulic integrity deterioration factor in the 0-1 interval.

7. The method according to claim 5, characterized in that, Based on the temporal evolution trajectory of chemical integrity degradation factors within historical cycles, a temporal extrapolation algorithm is used to predict the remaining time until the weed suppression function reaches critical failure, including: Obtain the time sequence of chemical integrity degradation factors within a preset historical period, as well as the trigger status record of the mutation penalty mechanism at each time point; the trigger status record is used to identify whether the mutation penalty mechanism has been triggered at each time point. Based on the trigger state record, the time series is divided into a gradual phase where mutation penalty is not triggered and an acceleration phase where mutation penalty is triggered; the chemical integrity degradation factor in the acceleration phase includes mutation penalty terms, while the chemical integrity degradation factor in the gradual phase does not include mutation penalty terms. For the chemical integrity degradation factor sequence in the gradual stage, the first decay model is used to fit its evolution trajectory; for the chemical integrity degradation factor sequence in the accelerated stage, the second decay model is used to fit its evolution trajectory; the decay rate of the second decay model is greater than the decay rate of the first decay model. Based on the segmented combination of the first decay model and the second decay model, the chemical integrity degradation factor value at future time is extrapolated and predicted. During the extrapolation process, when it is predicted that the current ratio of the first pH value to the second pH value at a future time is lower than the preset critical gradient ratio, the prediction model is switched from the first decay model to the second decay model at the future time, and a mutation penalty term is superimposed on the predicted value after the switch. The predicted chemical integrity degradation factor value is compared with the preset critical failure threshold of the herbicide suppression function to determine the moment when the predicted value first reaches the critical failure threshold of the herbicide suppression function. The time difference between that moment and the current moment is calculated as the remaining time.

8. The method according to claim 1, characterized in that, The basic alkali replenishment amount is determined based on the difference between the second pH value and the target restored pH value. This basic alkali replenishment amount is then corrected using the current volumetric moisture content of the substrate, generating an early warning instruction carrying recommended alkali replenishment information, including: Obtain the remaining time; when the remaining time is less than the preset maintenance window threshold, trigger the warning instruction generation process. Calculate the difference between the second pH value and the preset target restored pH value, and determine the basic amount of alkali to be added based on the preset mapping relationship of the amount of alkali to be added per unit pH difference; Obtain the current volumetric moisture content of the base layer, and determine the diffusion loss correction coefficient corresponding to the volumetric moisture content based on the preset moisture content correction mapping relationship; the diffusion loss correction coefficient increases non-linearly with the increase of volumetric moisture content. Multiply the basic alkali replenishment amount by the diffusion loss correction factor to obtain the final recommended alkali replenishment amount; Obtain the current hydraulic integrity degradation factor. When the hydraulic integrity degradation factor exceeds the preset hydraulic warning threshold, add a suggestion for strengthening the base structure to the warning instruction. The maintenance urgency level is determined based on the ratio of remaining time to the preset maintenance window threshold; the smaller the ratio, the higher the maintenance urgency level. Generate early warning instructions carrying the final recommended amount of alkali replenishment, the level of maintenance urgency, and recommendations for strengthening the base structure.

9. A high-standard farmland road base layer condition intelligent monitoring and early warning system, characterized in that, The system includes: The data acquisition module is used to acquire sensor array data at different vertical depths within the cross section of the lime-modified base layer, including at least the first pH value located on the surface of the base layer, the second pH value located at a preset depth inside the base layer, the volumetric moisture content of the base layer, and the mechanical response value of the base layer under vehicle load. The data construction module is used to construct a chemical integrity degradation factor characterizing the rate of loss of the grass-suppressing function of the base layer based on the relative decay difference between the first pH value and the second pH value in a continuous monitoring period; and to construct a hydraulic integrity degradation factor characterizing the degree of water stability damage of the base layer based on the correlation fluctuation characteristics of volumetric water content and mechanical response value within the same time window. The data processing module is used to compare the chemical integrity degradation factor and the hydraulic integrity degradation factor with the preset multi-level early warning threshold range, and at the same time compare the first pH value, the second pH value, the volumetric water content and the mechanical response value with the corresponding single-parameter critical threshold, and determine the early warning level according to the combination of comparison results. The data processing module is used to predict the remaining time before the weed suppression function reaches its critical failure based on the temporal evolution trajectory of the chemical integrity degradation factor within the historical cycle using a temporal extrapolation algorithm. When the remaining time is less than the preset maintenance window threshold, the basic alkali replenishment amount is determined based on the difference between the second pH value and the target recovery pH value, and the basic alkali replenishment amount is corrected using the current volumetric moisture content of the base layer, generating an early warning instruction carrying the recommended alkali replenishment amount information.

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